import Mathlib
import OAI.Computability.MaxCut.PCP.Overlap
import OAI.Computability.MaxCut.PCP.DistributionMaps

namespace OAI

/-!
# Finite local completion after correlated sampling

One independent seed contains the original shared seed and two full response
tables. Its local reads give exactly the mixture of the requested completion
kernels. Applying those kernels contracts total variation. On a diagonal
target the same construction uses one common selected label.
-/

namespace MaxCutGames.Foundations.Repetition.CompletedSampling

open scoped BigOperators
open Games

noncomputable section

section DistributionIdentities

variable {A B C D : Type*} [Fintype A] [Fintype B] [Fintype C] [Fintype D]

theorem mixture_pushforward_base (μ : FiniteDistribution A) (f : A → B)
    (K : B → FiniteDistribution C) :
    (μ.pushforward f).mixture K = μ.mixture (fun a => K (f a)) := by
  apply FiniteDistribution.eq_of_weight_eq
  intro c
  exact FiniteDistribution.expectation_pushforward μ f (fun b => (K b).weight c)

theorem product_pushforward_eq_mixture_right (μ : FiniteDistribution A)
    (ν : FiniteDistribution B) (f : A × B → C) :
    (μ.product ν).pushforward f =
      ν.mixture (fun b => μ.pushforward (fun a => f (a, b))) := by
  classical
  apply FiniteDistribution.eq_of_weight_eq
  intro c
  simp only [FiniteDistribution.pushforward, FiniteDistribution.product,
    FiniteDistribution.mixture, Fintype.sum_prod_type]
  rw [Finset.sum_comm]
  apply Finset.sum_congr rfl
  intro b _
  rw [Finset.mul_sum]
  apply Finset.sum_congr rfl
  intro a _
  by_cases h : f (a, b) = c <;> simp [h, mul_comm]

theorem product_product_pushforward_eq_mixture (μ : FiniteDistribution A)
    (ν : FiniteDistribution B) (κ : FiniteDistribution C) (f : A → B → C → D) :
    (μ.product (ν.product κ)).pushforward (fun z => f z.1 z.2.1 z.2.2) =
      (μ.product ν).mixture
        (fun z => κ.pushforward (fun c => f z.1 z.2 c)) := by
  classical
  apply FiniteDistribution.eq_of_weight_eq
  intro d
  simp only [FiniteDistribution.pushforward, FiniteDistribution.product,
    FiniteDistribution.mixture, Fintype.sum_prod_type]
  simp only [Finset.mul_sum, mul_ite, mul_zero, mul_assoc]

theorem mixture_totalVariation_le (μ ν : FiniteDistribution A)
    (K : A → FiniteDistribution B) :
    (μ.mixture K).totalVariation (ν.mixture K) ≤ μ.totalVariation ν := by
  simpa only [FiniteDistribution.totalVariation, FiniteDistribution.mixture,
    Information.totalVariation, kernelPushforward] using
      totalVariation_kernelPushforward_le μ.weight ν.weight
        (fun a => (K a).weight) (fun a => Information.gameLaw_isProbability (K a))

end DistributionIdentities

section Diagonal

variable {Z S : Type*} [Fintype Z] [Fintype S]

def diagonalLaw (σ : FiniteDistribution (Z × S)) : FiniteDistribution (Z × S × S) :=
  Information.toGameLaw (diagonalWeights σ.weight)
    (diagonalWeights_isProbability _ (Information.gameLaw_isProbability σ))

theorem diagonalLaw_eq_pushforward (σ : FiniteDistribution (Z × S)) :
    diagonalLaw σ = σ.pushforward (fun z => (z.1, z.2, z.2)) := by
  classical
  apply FiniteDistribution.eq_of_weight_eq
  rintro ⟨z, a, b⟩
  change diagonalWeights σ.weight (z, a, b) =
    (σ.pushforward (fun t => (t.1, t.2, t.2))).weight (z, a, b)
  simp only [diagonalWeights, FiniteDistribution.pushforward,
    Fintype.sum_prod_type, Prod.mk.injEq]
  by_cases hab : a = b
  · subst b
    simp [ite_and]
  · simp [hab, ite_and]

theorem diagonalLaw_mixture {T : Type*} [Fintype T]
    (σ : FiniteDistribution (Z × S)) (K : Z × S × S → FiniteDistribution T) :
    (diagonalLaw σ).mixture K = σ.mixture (fun z => K (z.1, z.2, z.2)) := by
  rw [diagonalLaw_eq_pushforward, mixture_pushforward_base]

end Diagonal

section Completion

variable {X Y S Γ U V : Type*}
  [Fintype X] [Fintype Y] [Fintype S] [Fintype Γ] [Fintype U] [Fintype V]
  [DecidableEq X] [DecidableEq Y] [DecidableEq S]

/-- The independent original seed and two full local completion tables. -/
abbrev Seed (Γ X Y S U V : Type*) :=
  Γ × KernelSampling.Seed (X × S) (Y × S) U V

def seedLaw (γ : FiniteDistribution Γ)
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V) :
    FiniteDistribution (Seed Γ X Y S U V) :=
  γ.product (KernelSampling.seedLaw L R)

def left (sL : Γ → X → S) (seed : Seed Γ X Y S U V) (x : X) : U :=
  KernelSampling.readLeft seed.2 (x, sL seed.1 x)

def right (sR : Γ → Y → S) (seed : Seed Γ X Y S U V) (y : Y) : V :=
  KernelSampling.readRight seed.2 (y, sR seed.1 y)

def completionKernel (L : X × S → FiniteDistribution U)
    (R : Y × S → FiniteDistribution V) (z : (X × Y) × S × S) :
    FiniteDistribution (U × V) :=
  (L (z.1.1, z.2.1)).product (R (z.1.2, z.2.2))

/-- This is the shared-seed mixture used by `Game.localEmbeddingLaw`. -/
def outputLaw (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V) :
    FiniteDistribution (U × V) :=
  (seedLaw γ L R).mixture (fun seed =>
    μ.pushforward (fun q => (left sL seed q.1, right sR seed q.2)))

theorem outputLaw_eq_product_pushforward
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V) :
    outputLaw μ γ sL sR L R =
      (μ.product (seedLaw γ L R)).pushforward
        (fun z => (left sL z.2 z.1.1, right sR z.2 z.1.2)) := by
  exact (product_pushforward_eq_mixture_right μ (seedLaw γ L R)
    (fun z : (X × Y) × Seed Γ X Y S U V =>
      (left sL z.2 z.1.1, right sR z.2 z.1.2))).symm

/-- The completion mixture is derived from actual local table reads. -/
theorem outputLaw_eq_mixture
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V) :
    outputLaw μ γ sL sR L R =
      (sharedOutputLaw μ γ sL sR).mixture (completionKernel L R) := by
  calc
    outputLaw μ γ sL sR L R =
        (μ.product γ).mixture (fun z =>
          (KernelSampling.seedLaw L R).pushforward (fun table =>
            (KernelSampling.readLeft table (z.1.1, sL z.2 z.1.1),
             KernelSampling.readRight table (z.1.2, sR z.2 z.1.2)))) := by
      rw [outputLaw_eq_product_pushforward]
      exact product_product_pushforward_eq_mixture μ γ (KernelSampling.seedLaw L R)
        (fun q seed table =>
          (KernelSampling.readLeft table (q.1, sL seed q.1),
           KernelSampling.readRight table (q.2, sR seed q.2)))
    _ = (μ.product γ).mixture (fun z =>
        completionKernel L R (z.1, sL z.2 z.1.1, sR z.2 z.1.2)) := by
      apply congrArg ((μ.product γ).mixture)
      funext z
      exact KernelSampling.read_joint_pushforward L R
        (z.1.1, sL z.2 z.1.1) (z.1.2, sR z.2 z.1.2)
    _ = (sharedOutputLaw μ γ sL sR).mixture (completionKernel L R) := by
      exact (mixture_pushforward_base (μ.product γ)
        (fun z => (z.1, sL z.2 z.1.1, sR z.2 z.1.2)) (completionKernel L R)).symm

omit [DecidableEq X] [DecidableEq Y] [DecidableEq S] in
/-- The diagonal target uses the same selected label for both completion rows. -/
theorem diagonalLaw_completion_mixture
    (σ : FiniteDistribution ((X × Y) × S))
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V) :
    (diagonalLaw σ).mixture (completionKernel L R) =
      σ.mixture (fun z => (L (z.1.1, z.2)).product (R (z.1.2, z.2))) := by
  exact diagonalLaw_mixture σ (completionKernel L R)

/-- Completing local outputs incurs no additional total-variation loss. -/
theorem outputLaw_totalVariation_le
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution U) (R : Y × S → FiniteDistribution V)
    (σ : FiniteDistribution ((X × Y) × S)) :
    (outputLaw μ γ sL sR L R).totalVariation
        (σ.mixture (fun z => (L (z.1.1, z.2)).product (R (z.1.2, z.2)))) ≤
      (sharedOutputLaw μ γ sL sR).totalVariation (diagonalLaw σ) := by
  rw [outputLaw_eq_mixture, ← diagonalLaw_completion_mixture σ L R]
  exact mixture_totalVariation_le _ _ (completionKernel L R)

end Completion

variable {I X Y S Γ : Type*}
  [Fintype I] [Fintype X] [Fintype Y] [Fintype S] [Fintype Γ]
  [DecidableEq I] [DecidableEq X] [DecidableEq Y] [DecidableEq S]

/-- Restore the left input coordinate even on completion tables of weight zero. -/
def coordinateLeft (j : I) (sL : Γ → X → S)
    (seed : Seed Γ X Y S (I → X) (I → Y)) (x : X) : I → X :=
  KernelSampling.completedLeft j seed.2 (x, sL seed.1 x)

/-- Restore the right input coordinate even on completion tables of weight zero. -/
def coordinateRight (j : I) (sR : Γ → Y → S)
    (seed : Seed Γ X Y S (I → X) (I → Y)) (y : Y) : I → Y :=
  KernelSampling.completedRight j seed.2 (y, sR seed.1 y)

omit [Fintype I] [Fintype X] [Fintype Y] [Fintype S] [Fintype Γ]
  [DecidableEq X] [DecidableEq Y] [DecidableEq S] in
@[simp] theorem coordinateLeft_preserves (j : I) (sL : Γ → X → S)
    (seed : Seed Γ X Y S (I → X) (I → Y)) (x : X) :
    coordinateLeft j sL seed x j = x := by
  simp [coordinateLeft]

omit [Fintype I] [Fintype X] [Fintype Y] [Fintype S] [Fintype Γ]
  [DecidableEq X] [DecidableEq Y] [DecidableEq S] in
@[simp] theorem coordinateRight_preserves (j : I) (sR : Γ → Y → S)
    (seed : Seed Γ X Y S (I → X) (I → Y)) (y : Y) :
    coordinateRight j sR seed y j = y := by
  simp [coordinateRight]

def coordinateOutputLaw (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (j : I) (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution (I → X))
    (R : Y × S → FiniteDistribution (I → Y)) :
    FiniteDistribution ((I → X) × (I → Y)) :=
  (seedLaw γ L R).mixture (fun seed => μ.pushforward
    (fun q => (coordinateLeft j sL seed q.1, coordinateRight j sR seed q.2)))

/-- Supported coordinate repair preserves the exact completion mixture. -/
theorem coordinateOutputLaw_eq_mixture
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (j : I) (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution (I → X))
    (R : Y × S → FiniteDistribution (I → Y))
    (hL : ∀ q xs, (L q).weight xs ≠ 0 → xs j = q.1)
    (hR : ∀ q ys, (R q).weight ys ≠ 0 → ys j = q.1) :
    coordinateOutputLaw μ γ j sL sR L R =
      (sharedOutputLaw μ γ sL sR).mixture (completionKernel L R) := by
  calc
    coordinateOutputLaw μ γ j sL sR L R =
        (μ.product (seedLaw γ L R)).pushforward
          (fun z => (coordinateLeft j sL z.2 z.1.1,
            coordinateRight j sR z.2 z.1.2)) := by
      exact (product_pushforward_eq_mixture_right μ (seedLaw γ L R)
        (fun z : (X × Y) × Seed Γ X Y S (I → X) (I → Y) =>
          (coordinateLeft j sL z.2 z.1.1, coordinateRight j sR z.2 z.1.2))).symm
    _ = (μ.product γ).mixture (fun z =>
        (KernelSampling.seedLaw L R).pushforward (fun table =>
          (KernelSampling.completedLeft j table (z.1.1, sL z.2 z.1.1),
           KernelSampling.completedRight j table (z.1.2, sR z.2 z.1.2)))) := by
      exact product_product_pushforward_eq_mixture μ γ (KernelSampling.seedLaw L R)
        (fun q seed table =>
          (KernelSampling.completedLeft j table (q.1, sL seed q.1),
           KernelSampling.completedRight j table (q.2, sR seed q.2)))
    _ = (μ.product γ).mixture (fun z =>
        completionKernel L R (z.1, sL z.2 z.1.1, sR z.2 z.1.2)) := by
      apply congrArg ((μ.product γ).mixture)
      funext z
      exact KernelSampling.completed_joint_pushforward L R j hL hR
        (z.1.1, sL z.2 z.1.1) (z.1.2, sR z.2 z.1.2)
    _ = (sharedOutputLaw μ γ sL sR).mixture (completionKernel L R) := by
      exact (mixture_pushforward_base (μ.product γ)
        (fun z => (z.1, sL z.2 z.1.1, sR z.2 z.1.2)) (completionKernel L R)).symm

theorem coordinateOutputLaw_eq_outputLaw
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (j : I) (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution (I → X))
    (R : Y × S → FiniteDistribution (I → Y))
    (hL : ∀ q xs, (L q).weight xs ≠ 0 → xs j = q.1)
    (hR : ∀ q ys, (R q).weight ys ≠ 0 → ys j = q.1) :
    coordinateOutputLaw μ γ j sL sR L R = outputLaw μ γ sL sR L R := by
  rw [coordinateOutputLaw_eq_mixture μ γ j sL sR L R hL hR, outputLaw_eq_mixture]

theorem coordinateOutputLaw_totalVariation_le
    (μ : FiniteDistribution (X × Y)) (γ : FiniteDistribution Γ)
    (j : I) (sL : Γ → X → S) (sR : Γ → Y → S)
    (L : X × S → FiniteDistribution (I → X))
    (R : Y × S → FiniteDistribution (I → Y))
    (hL : ∀ q xs, (L q).weight xs ≠ 0 → xs j = q.1)
    (hR : ∀ q ys, (R q).weight ys ≠ 0 → ys j = q.1)
    (σ : FiniteDistribution ((X × Y) × S)) :
    (coordinateOutputLaw μ γ j sL sR L R).totalVariation
        (σ.mixture (fun z => (L (z.1.1, z.2)).product (R (z.1.2, z.2)))) ≤
      (sharedOutputLaw μ γ sL sR).totalVariation (diagonalLaw σ) := by
  rw [coordinateOutputLaw_eq_outputLaw μ γ j sL sR L R hL hR]
  exact outputLaw_totalVariation_le μ γ sL sR L R σ

end

end MaxCutGames.Foundations.Repetition.CompletedSampling

/-!
Local embedding to single-game strategy transfer for Holenstein's argument.
The embedding law below is explicitly sampled from a finite shared seed and
the base game's actual joint question law. Its distance from the desired
conditioned law is an explicit total-variation expression. Constructing an
embedding with a sufficiently small distance is a separate proof obligation.
-/

namespace MaxCutGames.Foundations.Games.Game

noncomputable section

variable {Q₁ Q₂ A₁ A₂ R₁ R₂ B₁ B₂ Seed : Type*}
  [Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂]
  [Fintype R₁] [Fintype R₂] [Fintype B₁] [Fintype B₂] [Fintype Seed]
  [Nonempty A₁] [Nonempty A₂]

def localEmbeddingLaw (G : Game Q₁ Q₂ A₁ A₂)
    (seedLaw : FiniteDistribution Seed)
    (left : Seed → Q₁ → R₁) (right : Seed → Q₂ → R₂) :
    FiniteDistribution (R₁ × R₂) :=
  seedLaw.mixture fun seed =>
    G.questions.pushforward fun q => (left seed q.1, right seed q.2)

/-- Any finite shared-randomness local embedding yields a valid base-game
strategy after the seed is fixed. Total variation pays exactly for changing
the resulting question distribution to the desired one. -/
theorem success_le_value_add_embedding_distance
    (G : Game Q₁ Q₂ A₁ A₂) (H : Game R₁ R₂ B₁ B₂)
    (seedLaw : FiniteDistribution Seed)
    (left : Seed → Q₁ → R₁) (right : Seed → Q₂ → R₂)
    (answerLeft : Seed → Q₁ → B₁ → A₁)
    (answerRight : Seed → Q₂ → B₂ → A₂)
    (acceptance : ∀ seed x y a b,
      H.accepts (left seed x) (right seed y) a b = true →
      G.accepts x y (answerLeft seed x a) (answerRight seed y b) = true)
    (strategy : Strategy R₁ R₂ B₁ B₂) :
    H.success strategy ≤ G.value +
      H.questions.totalVariation (G.localEmbeddingLaw seedLaw left right) := by
  have localBound (seed : Seed) :
      (G.questions.pushforward (fun q => (left seed q.1, right seed q.2))).probability
        (H.wins strategy) ≤ G.value := by
    let embedded : Game R₁ R₂ B₁ B₂ :=
      { questions := G.questions.pushforward fun q => (left seed q.1, right seed q.2)
        accepts := H.accepts }
    change embedded.success strategy ≤ G.value
    exact (G.success_le_of_localSimulation embedded (left seed) (right seed)
      (answerLeft seed) (answerRight seed) rfl (acceptance seed) strategy).trans
      (G.success_le_value _)
  have mixtureBound :
      (G.localEmbeddingLaw seedLaw left right).probability (H.wins strategy) ≤ G.value :=
    seedLaw.probability_mixture_le _ _ _ localBound
  exact (H.questions.probability_le_add_totalVariation
    (G.localEmbeddingLaw seedLaw left right) (H.wins strategy)).trans
    (add_le_add mixtureBound (le_refl _))

/-- A single tested coordinate under an arbitrary full-tuple question law. -/
def coordinateGame (G : Game Q₁ Q₂ A₁ A₂) {n : Nat} (coordinate : Fin n)
    (law : FiniteDistribution ((Fin n → Q₁) × (Fin n → Q₂))) :
    Game (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂) where
  questions := law
  accepts x y a b := G.accepts (x coordinate) (y coordinate) (a coordinate) (b coordinate)

/-- The strategy-extraction step used for a surviving coordinate after
conditioning on successes at other coordinates. It preserves full local
question dependence and uses no independence assumption on the target law. -/
theorem coordinate_probability_le_value_add_embedding_distance
    (G : Game Q₁ Q₂ A₁ A₂) {n : Nat} (coordinate : Fin n)
    (law : FiniteDistribution ((Fin n → Q₁) × (Fin n → Q₂)))
    (seedLaw : FiniteDistribution Seed)
    (left : Seed → Q₁ → Fin n → Q₁) (right : Seed → Q₂ → Fin n → Q₂)
    (left_preserves : ∀ seed x, left seed x coordinate = x)
    (right_preserves : ∀ seed y, right seed y coordinate = y)
    (strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂)) :
    law.probability (G.coordinateWin strategy coordinate) ≤ G.value +
      law.totalVariation (G.localEmbeddingLaw seedLaw left right) := by
  apply G.success_le_value_add_embedding_distance (G.coordinateGame coordinate law)
    seedLaw left right (fun _ _ a => a coordinate) (fun _ _ b => b coordinate)
  intro seed x y a b h
  simpa only [coordinateGame, left_preserves, right_preserves] using h

/-- Finite samplers may have an arbitrarily small exhaustion error. Passing
to the limit in the numerical bound does not require an infinite seed or a
claim that any finite sampler terminates with probability one. -/
theorem coordinate_probability_le_value_of_approximate_embeddings
    {SeedFamily : Nat → Type*} [∀ t, Fintype (SeedFamily t)]
    (G : Game Q₁ Q₂ A₁ A₂) {n : Nat} (coordinate : Fin n)
    (law : FiniteDistribution ((Fin n → Q₁) × (Fin n → Q₂)))
    (seedLaw : (t : Nat) → FiniteDistribution (SeedFamily t))
    (left : (t : Nat) → SeedFamily t → Q₁ → Fin n → Q₁)
    (right : (t : Nat) → SeedFamily t → Q₂ → Fin n → Q₂)
    (left_preserves : ∀ t seed x, left t seed x coordinate = x)
    (right_preserves : ∀ t seed y, right t seed y coordinate = y)
    (strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
    (distance : ℝ)
    (close : ∀ η : ℝ, 0 < η → ∃ t,
      law.totalVariation (G.localEmbeddingLaw (seedLaw t) (left t) (right t)) ≤
        distance + η) :
    law.probability (G.coordinateWin strategy coordinate) ≤ G.value + distance := by
  by_contra h
  have gap : 0 < law.probability (G.coordinateWin strategy coordinate) -
      (G.value + distance) := sub_pos.mpr (lt_of_not_ge h)
  obtain ⟨t, ht⟩ := close
    ((law.probability (G.coordinateWin strategy coordinate) - (G.value + distance)) / 2)
    (by linarith)
  have bound := G.coordinate_probability_le_value_add_embedding_distance coordinate law
    (seedLaw t) (left t) (right t) (left_preserves t) (right_preserves t) strategy
  linarith

end
end MaxCutGames.Foundations.Games.Game

/-! An explicit finite shared sampler followed by supported local completion
gives the coordinate success bound. No embedding-existence premise remains. -/
namespace MaxCutGames.Foundations.Repetition
open scoped BigOperators
open Games CorrelatedSampling
noncomputable section
variable {Q₁ Q₂ A₁ A₂ S : Type*}
  [Fintype Q₁] [Fintype Q₂] [Fintype A₁] [Fintype A₂] [Fintype S]
  [Nonempty A₁] [Nonempty A₂] [Nonempty S]
  [DecidableEq Q₁] [DecidableEq Q₂] [DecidableEq S] {n : Nat}

theorem coordinate_probability_le_value_of_local_completion
    (G : Game Q₁ Q₂ A₁ A₂) (j : Fin n)
    (strategy : Strategy (Fin n → Q₁) (Fin n → Q₂) (Fin n → A₁) (Fin n → A₂))
    (σ : FiniteDistribution ((Q₁ × Q₂) × S))
    (profileL : Q₁ → FiniteDistribution S) (profileR : Q₂ → FiniteDistribution S)
    (completionL : Q₁ × S → FiniteDistribution (Fin n → Q₁))
    (completionR : Q₂ × S → FiniteDistribution (Fin n → Q₂))
    (supportL : ∀ q xs, (completionL q).weight xs ≠ 0 → xs j = q.1)
    (supportR : ∀ q ys, (completionR q).weight ys ≠ 0 → ys j = q.1)
    (fallback : S) :
    (σ.mixture (fun z => (completionL (z.1.1,z.2)).product
      (completionR (z.1.2,z.2)))).probability (G.coordinateWin strategy j) ≤
      G.value +
        (2 * Information.totalVariation σ.weight
          (fun z => G.questions.weight z.1 * (profileL z.1.1).weight z.2) +
         2 * Information.totalVariation σ.weight
          (fun z => G.questions.weight z.1 * (profileR z.1.2).weight z.2)) := by
  let targetLaw := σ.mixture (fun z => (completionL (z.1.1,z.2)).product (completionR (z.1.2,z.2)))
  let δ := 2 * Information.totalVariation σ.weight
      (fun z => G.questions.weight z.1 * (profileL z.1.1).weight z.2) +
    2 * Information.totalVariation σ.weight
      (fun z => G.questions.weight z.1 * (profileR z.1.2).weight z.2)
  change targetLaw.probability (G.coordinateWin strategy j) ≤ G.value + δ
  by_contra h
  have gap : 0 < targetLaw.probability (G.coordinateWin strategy j) - (G.value + δ) :=
    sub_pos.mpr (lt_of_not_ge h)
  let η := (targetLaw.probability (G.coordinateWin strategy j) - (G.value + δ)) / 2
  have hη : 0 < η := by dsimp [η]; linarith
  obtain ⟨N,hN⟩ := samplerOutputLaw_arbitrarily_close σ G.questions profileL profileR fallback η hη
  let thresholds := sharedProfileThresholds profileL profileR
  let γ := traceSeedLaw (rectangleDistribution (α := S) thresholds) N
  let sL := samplerLocal thresholds profileL fallback N
  let sR := samplerLocal thresholds profileR fallback N
  have hvariation := CompletedSampling.coordinateOutputLaw_totalVariation_le
    G.questions γ j sL sR completionL completionR supportL supportR σ
  have hclose : (CompletedSampling.coordinateOutputLaw G.questions γ j sL sR completionL completionR).totalVariation targetLaw ≤ δ + η :=
    hvariation.trans hN
  have hsuccess := G.coordinate_probability_le_value_add_embedding_distance j targetLaw
    (CompletedSampling.seedLaw γ completionL completionR)
    (CompletedSampling.coordinateLeft j sL) (CompletedSampling.coordinateRight j sR)
    (CompletedSampling.coordinateLeft_preserves j sL)
    (CompletedSampling.coordinateRight_preserves j sR) strategy
  have hsym : targetLaw.totalVariation
      (G.localEmbeddingLaw (CompletedSampling.seedLaw γ completionL completionR)
        (CompletedSampling.coordinateLeft j sL) (CompletedSampling.coordinateRight j sR)) =
      (CompletedSampling.coordinateOutputLaw G.questions γ j sL sR completionL completionR).totalVariation targetLaw := by
    unfold FiniteDistribution.totalVariation
    congr 1
    apply Finset.sum_congr rfl
    intro q _
    exact abs_sub_comm _ _
  rw [hsym] at hsuccess
  dsimp [η] at hclose
  linarith

end
end MaxCutGames.Foundations.Repetition

end OAI